(1) Field of the Invention
This invention relates to a signal processing system and method.
(2) Description of the Art
It is known to minimise an error function derived from a comparison of an output of a phased array antenna and a known training signal. In prior art systems each input channel is sampled in order to estimate a gradient of the error function in each dimension of the error function, as shown in FIG. 1. A system with n channels results in an n dimensional complex vector error function being generated.
Adaptive beam control strategies for phased array antennas commonly exploit a Least Mean Squares (LMS) algorithm for training a phased array antenna to form optimum beams. The LMS algorithm is an iterative update algorithm. This means that it progressively updates the weights of the array antenna in time in such a manner as to minimise a specified cost function. In the case of LMS the cost function is a mean squared error which is defined as the difference between a beamformed output of the antenna and a known training sequence. The cost function is reduced by estimating the gradient of an error surface defined by the n-dimensional error function and then adjusting the array weights at each iteration of the algorithm so as to progressively reduce the error. The final solution will approach that of the optimum Wiener-Hopf solution. The LMS algorithm now is well understood.
Such adaptive control of array antennas is an attractive proposition for future high data-rate wireless local area networks. Benefits include co-channel interference suppression, electronically steerable directional gain and optimal combining. Array antenna control algorithms include the Least Mean Squares (LMS), Recursive Least Squares (RLS) and Direct Matrix Inversion (DMI) techniques.
It is usual to employ a least means square (LMS) minimisation, or other minimisation technique, in n-dimensions in order to reach a global minimum in the error function. As the minimisation calculations are typically performed digitally each input channel must have its analogue voltage sampled and digitised. This necessitates each channel having its own analogue to digital converter (ADC) which must be powered. In the case of a large number of input channels the power required to run the ADC's can become significant. This is particularly problematic in the case of mobile devices that have a limited power supply, for example a mobile telephone having a battery. A further problem associated with current sampling arrangements is that the necessity of each input channel having its own ADC adds significantly to the complexity and the cost of the system. This can be especially crucial in low cost, high volume communication devices, such as Bluetooth enabled devices where each Bluetooth chip typically currently (2002) costs around $5 and any additional costs are therefore significant.
Another problem associated with such systems is that they are computationally demanding as n-complex multiplications must be performed for each LMS iteration, in order to simultaneously minimise each of the n-vector dimensions (for a system with n channels in the array of the antenna).
In the case where the LMS technique is used it is the negative of the gradient of the error of the n dimensional error surface that is minimised according to the following equation:ωk+1=ωk+με′xn
Where
ωk is the complex weighting vector applied to the elements at the kth iteration.
μ is the step size.
ε′ is the complex conjugate of the error between the desired signal and the received signal.
xn is the complex vector sampled from the n input channels.